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patient data integrity and patient data matching in healthcare

Achieving Higher Patient Data Integrity Requires a Multi-Layered Approach

patient data integrity and patient data matching in healthcare

Improving patient data integrity in healthcare requires a multi-layered approach that addresses both data matching and more accurate patient identification.

The following guest post was written by David Cuberos, Enterprise Sales Consultant with RightPatient®

Patient Data Integrity and Duplicate Medical Records

It is a well known fact that inaccurate or incomplete data within a patient’s medical record can be a catastrophic risk to patient safety, not to mention a serious hospital liability. As a result, many hospitals and healthcare organizations across the industry are closely examining the integrity of their health data and taking steps to clean it, most by using third party probabalistic and deterministic de-duplication matching algorithms (often directly from their EHR providers) that search and identify possible duplicates for an automatic or manual merge.

Several key players in the healthcare industry including CHIME, AHIMA, HIMSS, and major EHR providers are beating the drum to improve patient identification and patient data matching, all important catalysts for the push to improve patient data integrity.

If you are a hospital or healthcare organization that is knee deep in the middle of a health IT initiative to help increase patient data integrity (especially in the context of prepping for participation in a local or regional health information exchange), you may want to stop and reassess your strategy.  The rush to cleanse “dirty data” from EHR and EMPI databases is often addressed by relying on an EHR vendor’s de-duplication algorithm which is supposed to search and identify these duplicate medical records and either automatically merge them if similarity thresholds are high, or pass them along to the HIM department for further follow up if they are low. 

This could be a very effective strategy to cleanse an EMPI to ensure patient data accuracy moving forward, but is it enough? Is relying on an EHR vendor’s de-duplication algorithm sufficient to achieve high levels of patient data integrity to confidently administer care?  It actually isn’t. A more effective strategy combines elements of a strong de-duplication algorithm with strong patient identification technology to ensure that patient data maintains its integrity.

Duplicate Medical Record Rates are Often Understated

The industry push for system-wide interoperability to advance the quality and effectiveness of healthcare for both individuals and the general population has been one of the main catalysts motivating healthcare organizations to clean and resolve duplicates but it also has revealed some kinks in the data integrity armor of many different medical record databases. Most hospitals we speak with either underestimate their actual duplicate medical rate, or do not understand how to properly calculate it based on the actual data they can access.  An AHIMA report entitled “Ensuring Data Integrity in Health Information Exchange” stated that:

“…on average an 8% duplicate rate existed in the master patient index (MPI) databases studied. The average duplicate record rate increased to 9.4% in the MPI databases with more than 1 million records. Additionally, the report identified that the duplicate record rates of the EMPI databases studied were as high as 39.1%.”

“High duplicate record rates within EMPI databases are commonly the result of loading unresolved duplicate records from contributing MPI files. EMPI systems that leverage advanced matching algorithms are designed to automatically link records from multiple systems if there is only one existing viable matching record. If the EMPI system identifies two or more viable matching records when loading a patient record, as is the case when an EMPI contains unresolved duplicate record sets, it must create a new patient record and flag it as an unresolved duplicate record set to be manually reviewed and resolved. Therefore, if care is not taken to resolve the existing EMPI duplicate records, the duplicate rate in an EMPI can grow significantly as additional MPI files are added.”

(AHIMA report, “Ensuring Data Integrity in Health Information Exchange”  http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049675.pdf)

Clearly, the importance of cleansing duplicate medical records from a database cannot be understated in the broader scope of improving patient data integrity but relying on an EHR vendor’s probabilistic matching algorithm as the only tool to clean and subsequently maintain accurate records may not always be the most effective strategy. Instead, healthcare organizations should consider a multi-layered approach to improving patient data integrity beyond relying exclusively on an EHR vendor’s de-duplication algorithm. Here’s why.

Why Patient Data Integrity is a Multi-Layered Approach

Often not clearly explained to healthcare organizations, EHR de-duplication algorithms allow end users to set matching thresholds to be more or less strict, which comes with trade-offs. The more strict the threshold is set, the less chance of a false match but the higher chance of a false reject. The less strict the algorithm is set, the lower the chance of a false reject but the higher the chance of false acceptance.

Translation: Often times hospitals who say they have a low duplicate medical record rate might have a strict false acceptance rate (FAR) threshold setting in their de-duplication algorithm. That may mean that there could be a significant amount of unknown duplicate medical records that are being falsely rejected. Obviously, this is a concern because these databases must be able to identify virtually every single duplicate medical record that may exist in order to achieve the highest level of patient data integrity.

So, what can healthcare organizations do to ensure they are not only holistically addressing duplicate medical record rates, but also adopting technology that will maintain high patient data integrity levels moving forward? One answer is to implement a stronger de-duplication algorithm that has the ability to “key” and link medical records across multiple healthcare providers on the back end, and deploying a technology such as biometrics for patient identification on the front end to ensure that not only is care attribution documented to the accurate medical record, but a provider has the ability to view all patient medical data prior to treatment. 

For example, many credit bureaus offer big data analytics solutions that can dig deep into a medical record database to better determine what identities are associated with medical records. These agencies are experts in identity management with access to sophisticated and comprehensive databases containing the identification profiles for millions and millions of patients — databases that are reliable, highly accurate, and secure with current and historical demographic data.

Once data is analyzed by these agencies, they are able to assign a “key” to match multiple medical records for the same patient within a single healthcare organization and across unaffiliated healthcare organizations to create a comprehensive EHR for any patient. Offering a unique ability to augment master patient index (MPI) matching capabilities with 3rd party data facilitates more accurate matching of medical records across disparate health systems and circumvents the problem of MPIs assigning their own unique identifiers to individual patients that are different than unaffiliated healthcare organizations that have their own MPI identifiers.

Benefits of using a third party big data analytics solution that has the ability to “key” medical records for more accurate patient data matching at a micro level include:

  • More accurate identification of unique patient records resulting in a more complete medical record and improved outcomes
  • Prevention of duplicate medical records and overlays at registration reduces the cost of ongoing MPI cleanups
  • Medical malpractice risk mitigation 
  • Reduced patient registration times
  • The ability to more accurately link the most current insurance coverage patient information for more accurate billing

On the marco level, benefits include: 

  • Positive patient identification for eligibility verification, billing, coordination of benefits, and reimbursement
  • Improved care coordination
  • Information and record keeping organization 
  • Linkage of lifelong health records across disparate healthcare facilities
  • Aggregation of health data for analysis and research
  • Accurately aggregating patient federated data via a HIE

Conclusion

We have long championed the idea that improving patient data integrity can never be achieved in the absence of establishing patient identification accuracy or relying on EHR vendor de-duplication algorithms as the single resource to clean an MPI database. Hospitals and healthcare organizations that are truly committed to cleansing duplicate medical records from their databases and preventing them from reoccurring through more accurate patient identification must consider deploying stronger front and back end solutions that have the ability to more comprehensively identify and resolve these dangers to patient safety. Why not leverage the clout and reach of these big data analytics solutions to more effectively improve patient data integrity instead of putting all of your eggs in an EHR vendor’s de-duplication algorithm?

What other strategies have you seen as effective methods to increase patient data integrity in healthcare?

biometric patient identification prevents duplicate medical recordsDavid Cuberos is an Enterprise Sales Consultant with RightPatient® helping hospitals and healthcare organizations realize the benefits of implementing biometrics for patient identification to; increase patient safety, eliminate duplicate medical records and overlays, and prevent medical identity theft and healthcare fraud.

biometric patient identification solutions prevent duplicate medical records and overlays

New Podcast: The Impact of Duplicates and Overlays on Health Information Management (HIM)

biometric patient identification solutions prevent duplicate medical records and overlays

Our latest podcast features HIM Director Erin Head discussing the impact of duplicate medical records and overlays on health information management (HIM).

Erin brings a wealth of experience to health information management (HIM) work flow and managing patient data integrity so naturally we were excited to tap into her knowledge base to better understand the HIM “front line” – a deeper discussion about the day to day activities in the trenches and a firsthand account of the negative impact of duplicate medical record and overlay identification and reconciliation. Our conversation with Erin covered the following topics:

— How duplicate medical record reconciliation impacts HIM workflow and other job responsibilities sacrificed due to duplicate/overlay reconciliation

— The average FTEs health information management spends reconciling duplicates and overlays and the financial impact on the hospital if FTE’s that are currently cleaning up duplicates and overlays could be reallocated to more revenue generating activities such as coding

— How the shift to quality vs. quantity based care impacts the responsibilities and sense of urgency for HIM

— Whether the ONC cost estimate of $60 per duplicate record is low or high compared to her own experience

— The impact on HIPAA violations that duplicates/overlays cause and the cost if a hospital releases information to wrong patient

— How the introduction of the patient portal complicates management of duplicates

— How the implementation of a biometric patient identification system helps to lower the burden of reconciling duplicates and overlays and allows health information management to focus on their core competencies

For a full version of the podcast, please visit the landing page for more information. 

Have an idea for a podcast or know a healthcare professional that would be a good candidate to interview? Email us at: info@rightpatient.com with your ideas!

patient matching and patient identification in healthcare

Healthcare Scene Blab Tackles Patient Matching and Patient Identification

patient matching and patient identification in healthcare

Healthcare Scene’s John Lynn hosts a blab conversation on the topic of patient matching in healthcare with Michael Trader and Beth Just.

Our President Michael Trader was grateful for opportunity to discuss patient matching and patient identification in healthcare with Beth Just from Just Associates during John Lynn’s blab session earlier today. The discussion covered a wide range of topics including:

— How big is the patient identification problem in healthcare?
— The continuing problem of duplicate medical records in healthcare and strategies to improve and sustain patient data integrity
— Describing the availability and measuring the success of existing patient identification solutions in healthcare 
— Would a national patient identifier help or would the existing challenges still apply?
— Why can’t the current solutions get to 100% patient matching?
— How does the CHIME $1 million National Patient ID Challenge work?Is this challenge achievable? 

What materialized was an excellent discussion on patient identification in healthcare with both Michael and Beth offering intelligent insight on the problems that exist, solutions built to address the problems, and what it truly means to achieve 100% patient ID accuracy. Take a moment to watch the blab session here:

 

Special thanks to John Lynn and Healthcare Scene for hosting the discussion! 

What are your top concerns surrounding the issue of achieving 100% patient matching in healthcare? Please share them with us in the comments below.

accurate patient matching in healthcare through reconciling duplicate medical records

AHIMA Survey on Patient Matching Illustrates HIM Burdens, Frustrations

accurate patient matching in healthcare through reconciling duplicate medical records

A recent survey of HIM professionals by AHIMA illustrates the problems that duplicate medical records have on accurate patient matching in healthcare. (Photo courtesy of Wikimedia Commons: http://bit.ly/2hVSvMc)

The following post was submitted by Brad Marshall, Enterprise Development Consultant with RightPatient®

AHIMA Sheds Light on Patient Matching Problems in Healthcare

The American Health Information Management Society (AHIMA) released details of a survey yesterday that revealed over half of Health Information Management (HIM) professionals still spend a significant amount of time reconciling duplicate medical records at their respective healthcare facilities. The survey went on to reveal some very interesting statistics on patient matching and linking patient records, illustrating the burden that duplicate medical records have not only on HIM staff, but the dangers care providers face who increasingly rely on access to accurate, holistic patient data to provide safe, quality care. One particular stat that jumped out at us was:

“…less than half (47 percent) of respondents state they have a quality assurance step in their registration or post registration process, and face a lack of resources to adequately correct duplicates.”

This is an area of particular concern due to the fact that our research has shown that many healthcare facilities spend tens, sometimes hundreds of thousands of dollars per year reconciling duplicate medical records but very few have technology in place to prevent duplicates in the future. It’s encouraging that accurate patient matching in healthcare seems to finally be getting the attention it deserves, due to the digitization of the industry, the shift change from fee-for-service to a value based payment system and a burgeoning healthcare ecosystem laser focused on improving both individual outcomes and population health. AHIMA’s survey supports this assertion by stating:

“Accurate patient matching “underpins and enables the success of all strategic initiatives in healthcare…”

Equally concerning is the fact that less than half of HIM professionals surveyed have any type of patient registration quality assurance policy in place and only slightly over half of survey respondents could accurately say what their duplicate medical rate actually is. Not to mention the fact that HIM professionals spend entirely too much of their time reconciling duplicate medical records, with 73% reporting that they work duplicates “at a minimum of weekly.” 

As more healthcare organizations and facilities begin to understand that accurate patient matching has a major impact on other downstream activities, it is encouraging that the issue is finally getting the attention it deserves helped in part by the efforts of AHIMA, and CHIME’s national patient identification challenge which is scheduled to kick off this month.  It’s clear that the healthcare industry is slowly coming to the realization that many new initiatives borne from the HiTech Act and Meaningful Use (e.g. – population health, ACOs, health information exchanges, interoperability) don’t really have any hope to succeed in the absence of accurate patient identification. 

Duplicate Reconciliation Unnecessary Burden on HIM?

Early last year, we wrote a blog post on How Accurate Patient Identification Impacts Health Information Management (HIM) which highlights the exorbitant amount of time HIM spends reconciling duplicates and the opportunity cost this brings. For example, time spent on duplicate clean up and reconciliation could instead be allocated to coding for reimbursement and preparing, indexing, and imaging all paper medical records – a critical component in the effort to capture and transfer as much health data as possible to a patient’s EHR.

The fact of the matter is that as health data integrity stewards and medical record gatekeepers, HIM professionals are better served spending their time ensuring proper and accurate reimbursement and medical record accuracy then reconciling duplicates which should have never been created in the first place. HIM staff perform one of if not the most critical functions in healthcare by ensuring health data integrity, especially in light of the increasing reliance of often disparate healthcare providers need to access a complete medical record that includes as much information as possible.

As we noted in the post last January:

“…many hospitals have expanded responsibilities vis-à-vis Meaningful Use, EHR implementation, and meeting Affordable Care Act requirements, and it has become disadvantageous to continue devoting any time at all to duplicate medical record and overlay reconciliation. Biometric patient identification solutions open the door to re-allocation of HIM FTEs to more critical functions such as coding, reimbursement, and reporting. Simply put, implementing biometrics during patient registration is opening the door for HIM departments across the industry to provide a larger and more productive support role to meet the shifting sands of reimbursement and address the need to move towards quality vs. quantity of care.”

Conclusion

We could not have summed up the issue of duplicate medical record creation and reconciliation and inaccurate patient matching in healthcare more succinctly than this quote from AHIMA in the survey summary:

“Reliable and accurate calculation of the duplicate rate is foundational to developing trusted data, reducing potential patient safety risks and measuring return on investments for strategic healthcare initiatives.” 

Trusted data. Isn’t this the backbone of the new healthcare paradigm? Certainly we can’t expect to achieve many of the purported advances in healthcare in the absence of clean, accurate health data. It’s time to eliminate duplicate medical records forever, and establish cohesive, quality assured patient matching in healthcare.

What are your biggest takeaways from the AHIMA report on accurate patient matching in healthcare?

Brad Marshall works for RightPatient - the industry's best biometric patient identification solution.Brad Marshall is an Enterprise Development Consultant with RightPatient®. With several years of experience implementing both large and small scale biometric patient identification projects in healthcare, Brad works closely with key hospital executives and front line staff to ensure project success.

 

accurate patient identification in healthcare eliminates duplicate medical records and improves revenue cycle management

Improving Revenue Cycle Management with Accurate Patient ID

accurate patient identification in healthcare eliminates duplicate medical records and improves revenue cycle management

Eliminating duplicate medical records to improve revenue cycle management is achieved through accurate patient identification. (Photo courtesy of Pixabay: http://bit.ly/1L7A51U)

The following post was submitted by Jeremy Floyd, Healthcare Director at RightPatient®.

The Dangers of Duplicate Medical Records

Most of us already know that duplicate medical records in healthcare pose a direct threat to patient safety. The concept is rather straightforward — if a duplicate medical record exists for a patient within an electronic health record (EHR) database or master patient index (MPI), chances are that clinicians may make a medical error based on a fragmented view of a patient’s medical history.  There are myriad reasons why a duplicate medical record may exist ranging from patient names that have complex spellings and/ or variations of a name, data entry input errors by hospital staff, identity sharing among patients, and unenforced admissions quality standards across a provider network. 

Duplicate medical records can be created from the simplest of errors — using nicknames to identify a patient or a missing digit on a social security number, date of birth, or address for example. Often times, the problem of duplicate medical records is most prevalent with patients who have similar or identical names.

Compounding the problem of duplicate medical records in healthcare is the shift change of healthcare providers from single entities to complex integrated delivery networks (IDNs) and Accountable Care Organizations (ACOs) which require that patient records contained in multiple MPIs be aggregated into a single Enterprise Master Patient Index (EMPI) to provide a holistic view of the patient’s record across the care continuum. Unfortunately, many healthcare organizations are unaware of the complex variations in how a person is demographically represented in multiple records in different systems. Consequently, when basic matching criteria is used on various combinations of a person’s name, date of birth, gender, and social security number, the end result is patient records with multiple typographical errors, or different representations of a person’s name as un-matched duplicates in the resulting EMPI. 

It becomes quite clear that the evolution of healthcare to expand data sharing that benefits both individual and population health is exacerbating the risks that duplicate medical records have on the ability to provide safe and accurate care not to mention placing financial constraints that inhibit the flow of accounts receivable.

The Hidden Effect of Duplicates on Revenue Cycle Management

We talk a lot about how duplicate medical records negatively impact patient safety.  We know that their presence can easily create unnecessary medical errors and weaken patient data integrity. We also understand that the bulk of duplicate medical records are created by patient misidentification.

What is often overlooked and not discussed enough is the effect that duplicate medical records have on efficient revenue cycle management. The Healthcare Financial Management Association (HFMA) recently wrote about the inverse relationship between duplicate medical records and revenue cycle management stating that, “Lowering the duplicate patient record rate increases revenue cycle efficiency by improving the accuracy of information used to submit claims, collect payments, and provide care.” (Source:  http://www.hfma.org/Content.aspx?id=16788

The fact is that the negative impact of duplicate medical records extends far beyond patient safety, affecting many other “downstream financial activities” — as HFMA states in their article. In other words, duplicates pose a direct threat to financial stability and efficiency because their existence leads to medical reporting inaccuracies and repeat testing that insurance companies will not reimburse. Plus, duplicates can negatively affect or even sabotage other hospital initiatives that rely on high levels of patient data integrity — the implementation of an EHR system for example. HFMA notes that that many other downstream activities can be affected by duplicates, specifically:

  • Inefficient use of medical records staff time on correcting duplicates rather than focusing on coding
  • Delayed claims payments or denials due to the use of an incorrect name or other identifiers, or for duplicated services
  • Higher A/R days due to late payments
  • Patient safety risks when the duplicate record does not include all important information, especially items such as medication allergies, diagnostic test results, or previous diagnoses
    (Source: http://www.hfma.org/Content.aspx?id=16788)

What’s clear is that the most likely source of duplicate creation is patient registration leading many healthcare organizations to more closely evaluate best practices and existing workflow and identify areas of improvement. Many are also implementing modern patient identification technologies that eliminate duplicate medical records by removing the ability to create them in the first place. 

Using Accurate Patient Identification to Increase Revenue Cycle Efficiency

Perhaps one of the hottest topics to surface in the wake of healthcare digitization is the absence of static patient identifiers, especially in the context of exchanging patient information quickly, affordably, and safely. Patient matching inconsistencies have bubbled to the surface in many broader discussions about establishing efficiencies in healthcare — most notably for healthcare information exchange and information governance. However, recognizing the need to establish tighter control over accurate patient identification should first be defined in the context of how it will improve internal initiatives (e.g. – revenue cycle management) before expanding applicability to projects that provide data sharing to a larger provider demographic.

Among the numerous options available to help identify and reduce duplicate medical records and improve patient identification in healthcare is the use of deterministic or probabilistic data matching. Although these methods are relatively sufficient to clean MPIs of duplicates, the disconnect seems to be implementing a more secure and accurate patient identification technology on the front end to sustain a clean MPI moving forward. Remember that there is a distinct difference between identifying and cleansing an MPI of duplicates, and deploying another strategy to ensure that a database remains clean. This is where many healthcare providers fall short.

The most effective approach to eradicating duplicate medical records and improve revenue cycle management is evaluating modern patient identification solutions that are powerful enough to sustain a clean MPI and prevent some of the aforementioned downstream repercussions that can damage financial health. After all, a fluid and efficient revenue cycle management system uninhibited by the impact of duplicate medical records helps to keep costs down and improve the quality of care.

RightPatient is a smart health platform thatJeremy has worked in the biometrics industry for nearly a decade and has real world experience with fingerprint, palm vein, finger vein, iris and face recognition technologies. He currently oversees the RightPatient™ Healthcare division of M2SYS Technology, including sales, business development and project management. Before taking over the Healthcare unit, Jeremy spearheaded the growth of the core biometrics division, working closely with Fortune 500 clients like ADP, JP Morgan & BAE Systems to implement biometrics in large identity management projects. 

biometrics for patient identification should be a priority project

Biometric Patient Identification Implementation Should Be Higher On The Priority List

biometric patient identification should be a priority project

Are healthcare organizations evaluating the proper criteria to prioritize enterprise IT Projects? (Photo courtesy of pixabay: http://bit.ly/2ihfSvh)

The following post was submitted by Brad Marshall, Enterprise Development Consultant with RightPatient®

As someone with a long track record of implementing enterprise IT solutions in healthcare, I frequently observe hospitals “shuffling the project deck” as they compare and contrast the merits and return on investment (ROI) of each initiative in order to determine which makes the most sense to allocate budget dollars. Does politics at times play a part in the decision of which technology projects eventually get approved? Yes, at times. Are there often misinterpretations of the value that an enterprise IT project can offer in both the short and long term? Absolutely. Do hospitals often place a high priority on implementing projects that in reality, should be pushed further down the list in lieu of the value that another project brings to the table? Definitely.

Case in point: the implementation of biometrics for patient identification in healthcare. Although I am obviously biased towards this technology since I work for a company that has helped many hospitals throughout the world see the benefits of using it to increase patient safety, prevent duplicates and overlays, and protect patients from medical identity theft and fraud, it doesn’t alter the facts about how implementing biometrics before making a commitment to other competing enterprise software projects is something more healthcare organizations should consider. Why? Let’s look at some specific examples of alternate enterprise project implementations that would actually benefit and see performance improvements if a biometric patient identification project deployment took preference:

1. EHR Projects – Although the implementation or “switch” to another EHR vendor is perhaps one of the most complicated, time consuming, and resource-intensive enterprise software projects a healthcare organization will ever undertake, the implementation of a biometric patient identification system prior to embarking on an EHR project is a smart idea. Why?

Our experience has shown that there is always an uptick in duplicate medical records when you are initially implementing or switching to a new EHR system, primarily because staff are adjusting to new workflows and are less likely to catch duplicates the first few months. Since duplicate medical records present a direct threat to patient safety and a serious treatment error risk to healthcare organizations, their prevention should be priority #1 for any modern EHR system. 

I am not necessarily advocating the push for budget dollar allocation to biometric patient identification over an EHR project, however implementing a biometric patient identification solution before an EHR Go Live will make it more successful by immediately eliminating the possibility of creating duplicate medical records and overlays and prevent staff from making registration and patient identification mistakes while learning the new system.

2. Duplicate Medical Record “Clean-Up” –  Often times, I run across hospitals that may be evaluating the implementation of a “duplicate medical record clean-up” project prior to deploying biometrcis for patient identification. Without discounting the importance of purging duplicate medical records from any EHR database, the argument for why hospitals should consider the use of biometrics for patient identification is clear — healthcare organizations will successfully perform a “de-duplication” cleanup but continue creating duplicates until they implement stronger patient ID technology and will most likely have to do another cleanup down the road. 

Keep in mind that it only takes one – ONE – mistreatment at the hands of incorrect, missing, or incomplete medical data due to duplicates or overlays to result in harm, or possibly even death of a patient. Ask yourself, are you willing to assume the risk of medical errors to patients and the repercussions (which often can include hundreds of thousands, even millions of dollars in legal fees and compensation) of these errors for the short term gain of a “clean” master patient index (MPI)? Chances are, you aren’t willing to take that risk which leads to a stronger argument to implement biometrics for patient ID prior to launching a duplicate medical record clean-up initiative.

3. Big Data and Analytics – These are project priorities that perhaps perplex me the most, especially in the context of establishing higher data integrity when preparing to join a health information exchange (HIE), or as part of a merger that joins separate Integrated Delivery Networks (IDNs).  If a healthcare organization is seeking to allocate budget dollars to initiatives that advance data integrity, that’s good news. No one will argue that the healthcare industry simply has to better understand and find wisdom in the terabytes of data their systems possess to help advance the “triple aim” and deliver higher quality care, especially as more disparate networks are attempting to share data . However, the problem is that allocating budget dollars to these deployments is the quintessential “cart before the horse” mentality.

Instead of placing more emphasis on cleaning existing “dirty” data, healthcare organizations are rushing to the HIE table for fear of losing a seat or appearing indifferent to their patients and the industry wide push on sharing and making health data more accessible. What good is joining a HIE (or merging IDN’s) in the absence of technology that ensures that not only is the data you share clean and all medical data is properly attributed to the correct patient, but also guarantees that the data will STAY clean to give you the confidence that clinicians truly have a complete picture of a patient’s health and medical history when administering care. 

I’m reminded of a story that is a perfect illustration of why implementing biometrics for patient ID should take precedence over many other health IT projects, especially those that address data quality.

Years ago I worked for a local YMCA that had a leaky roof over the gymnasium. Each time it would rain heavily, staff would be scrambling to place buckets around the gym floor that would strategically catch the water leaking from the roof. The leaks would cause event and class cancellations, disrupt workout schedules, and generally leave paying members feeling a bit frustrated. YMCA management then made a decision to replace the aging, wooden gym floor with a new model that was built with a soft rubber substance – a radical new technology that was supposed lower the impact and strain of running on a hardwood surface. They then spent tens of thousands of dollars replacing the floor, and as you may have guessed, the next time a powerful storm came through, it leaked water all over the new gym floor. The irony in this situation of course is that management should have allocated the funding to fix the roof before they had the new floor installed.

As we continue to help the healthcare industry understand the advantages of implementing biometrics for patient identification, we understand that many healthcare organizations are not flush with cash to haphazardly allocate to any enterprise project that comes down the road. There are many mission critical projects that simply take precedence in the broader scope of improving the quality of care. Shouldn’t the deployment of biometrics for patient identification be one of them?

Brad Marshall works for RightPatient - the industry's best biometric patient identification solution.

Brad Marshall is an Enterprise Development Consultant with RightPatient®. With several years of experience implementing both large and small scale biometric patient identification projects in healthcare, Brad works closely with key hospital executives and front line staff to ensure project success.

 

the use of biometrics for patient identification is increasing in the healthcare industry

Fortune Magazine Article Highlights Growing Use of Biometrics for Patient Identification

the use of biometrics for patient identification is increasing in the healthcare industry

A patient has their photo captured with an iris recognition camera at a hospital that has deployed biometrics for patient identification.

Excellent article in Fortune magazine today written by Laura Shin that addresses the topic of healthcare data breaches and whether or not the increasing use of biometrics for patient identification will add a layer of protection to help thwart hackers in the future and eliminate medical identity theft and healthcare fraud. 

We are grateful that Laura included us in her research for the article, mentioning our work with implementing iris biometrics for patient identification at Novant Health’s Clemmons Medical Center location and a specific case of when a father brought his son into their facility, pointing out that: Read more